shap by shap

A game theoretic approach to explain the output of any machine learning model.

updated at May 12, 2024, 5:24 a.m.

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shapash by MAIF

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

updated at May 11, 2024, 8:42 a.m.

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what-if-tool by PAIR-code

Source code/webpage/demos for the What-If Tool

updated at May 10, 2024, 6:03 a.m.

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xai by EthicalML

XAI - An eXplainability toolbox for machine learning

updated at May 10, 2024, 2:44 a.m.

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